The subject of the present invention is a method of image analysis the aim of which is to improve the readability of analyzed images. It applies to the processing of all images, in one, two or three dimensions, and it is particularly useful in the medical sector. It relates however to all sectors in which images are acquired, by any means of measurement, of a physical quantity of an object studied. This measurement results in an image signal. The physical quantity may be a visible quantity, and the method then relates, for example, to images acquired with a television camera. Or else it may be an invisible quantity, for example, the coefficient of absorption of X-rays in a tissue, but transformed into a visible quantity through processing. The images concerned will preferably be digitized images since the computer processing of such images is simpler. However, according to the method of the invention, analog processing is also possible.
One of the applications of the invention is a method of contouring objects in an image, for example with a known processing termed WATERSHED (WPL), as published in IMAGE SEGMENTATION AND MATHEMATICAL MORPHOLOGY, thesis from the Ecole des Mines de Paris, S. BEUCHER, June 1990. This uses minima or maxima selected for example with the method of the invention in a first step, for locating the objects to be contoured. These selected minima or maxima are therefore markers found automatically. Once these are located, the automatic extraction of their contour is undertaken with the WPL algorithm. The latter transformation is itself implemented successfully with the aid of a method termed processing by HIERARCHICAL QUEUES or FAH published in "An optimal watershed algorithm", Fernand MEYER, Proceeding Congress AFCET, Lyon, November 91, page 847-859, the subject matter of which is hereby incorporated by reference.
Indeed, in automatic contouring methods it is sometimes necessary to mark beforehand in the image the zones of this image which are to be delimited. This marking can be manual. It then leads to a rather impractical procedure of interaction between the operator and the processing machine. It may preferably itself be automatic. However, so as to render the marked image points, or markers, significant, the latter are determined according to the invention by comparing their dynamic with a threshold.
In another application, the invention serves to locate in an angiograph image the presence of vessels for the purposes of filtering. It is thus sought to eliminate therein weakly contrasted structures which do not represent vessels. Other applications are also possible. To clarify the invention, and to give a complete explanation thereof, it will be described in the context of the angiogram application.
The main defect of images is materialized through the presence of noise. The result of noise is to bring about an alteration in the value of the signal of the image at each point of the image. This alteration follows a statistical law. The alteration can be positive or negative. One of the characteristics of noise is that it is incoherent. The alteration at an image point is thus uncorrelated with the alterations of the image signal at a neighboring point in the image. This incoherence has induced an image processing aimed at eliminating the noise. This processing is a processing through which, in principle, the image signal at a point of the image is replaced by a combination (for example an average) of the signals in image points neighboring this point. This processing is equivalent to a spatial filtering.
Such a spatial filtering has a disadvantage inherent in its principle. In effect, it blocks out the presence of small structures since the image signal from these small structures is damped by the image signal from the structures which neighbor these small structures. In the end they disappear. This is particularly awkward when, for example in detecting cancer of the breast, it is attempted to reveal the presence of microcalcifications inside a tissue. The image signal of these micro-calcifications is then averaged with the neighboring tissue image signal. As a result, it is no longer seen.
To resolve this problem in another way, an attempt has already been made to replace the spatial filtering by a contrast processing in which all the minima or maxima of the image signal are plotted. A minimum or maximum in the image signal is a value of the image signal corresponding to a particular place in the image. By extension, this place is then itself said to be a minimum or a maximum of the image. For this particular place in the image, all the directly neighboring image points contiguous with this point have image signals whose value is larger (or smaller in the case of a maximum). A minimum (or a maximum) can also be a set of neighboring points, of equal image signal value, and such that every neighbor of this set has an image signal whose value is strictly greater than (respectively less than) that of the image signal of the points of the set. This set of neighboring points of equal signal value is called a plateau. An image point is regarded as a direct neighbor of another image point when no intermediate points can be found between this point and its direct neighbor. In the case of digitized image signals, the neighboring samples of the signal refer of course to neighboring image points. A maximum in the image signal corresponds, under the same conditions, to a place in the image where all the direct neighbors have smaller image signals. Such a contrast processing is unfortunately ineffective since it sets on an equal footing the alterations in the image signal resulting from noise and the modifications of this signal resulting from the presence of structures which it is desired to show.
In the remainder of this description, for simplicity a video signal will be presented--an analog television line. The principle of the processing operations of the invention will be explained for such a signal. This processing will then be applied to a two-dimensional image. Finally, by explaining the digital processing of such an analog signal, a generalization thereof to three dimensions will be presented.
In a television signal, which will be described as a monochrome signal for simplicity, a temporal change (related to a spatial description of a line) is observed in a luminance signal. This luminance signal has minima and maxima. In order to locate them, it is possible to inspect, for each point, or rather for each plateau, whether the two neighbors at the ends of the plateau have lower or higher luminance values. If this is the case, the point is said to be a maximum (or a minimum). For a two-dimensional image, an analogous procedure can be undertaken and a luminance value will be tested of all the points on the boundary of the plateau. Other solutions for locating maxima (or minima) exist as for example that which consists in making a digital geodesic reconstruction. This technique is also published in the first article cited. This step will not be detailed insistently, since all the methods lead to the same result.